We present results from successful implementation of long-term (\>10 seconds) real-time integration of acceleration to measure position. We evaluated two analog circuit designs for double integration of an acceleration signal. Our circuit design features three high-pass filters and two first order integrators, leading to a critically damped double integrator. The second design we have implemented is a second order underdamped integrator reported in the literature. Accuracy of both circuits in sensing position based on only accelerometer readings was experimentally evaluated by comparison with encoder readings. We conclude that a critically damped double integrator coupled with an accelerometer is well-suited for frequency domain system identification, thanks to reliable position measurement capability with minimal interference to system dynamics.

Recent literature suggests that inducing proprioceptive movement illusions with predefined movement trajectories via tendon vibration requires use of multiple vibrators and precisely controlled frequency profiles. In this study, we report the design, modeling and control of a compact, low-cost tendon vibrator and illustrate its capability of accurately following time-varying frequency profiles. During the demonstration, participants will test the vibrator to experience illusory elbow flexion.

This paper introduces and validates quantitative performance measures for a rhythmic target-hitting task. These performance measures are derived from a detailed analysis of human performance during a month-long training experiment where participants learned to operate a 2-DOF haptic interface in a virtual environment to execute a manual control task. The motivation for the analysis presented in this paper is to determine measures of participant performance that capture the key skills of the task. This analysis of performance indicates that two quantitative measures{\textemdash}trajectory error and input frequency{\textemdash}capture the key skills of the target-hitting task, as the results show a strong correlation between the performance measures and the task objective of maximizing target hits. The performance trends were further explored by grouping the participants based on expertise and examining trends during training in terms of these measures. In future work, these measures will be used as inputs to a haptic guidance scheme that adjusts its control gains based on a real-time assessment of human performance of the task. Such guidance schemes will be incorporated into virtual training environments for humans to develop manual skills for domains such as surgery, physical therapy, and sports.

We hypothesized that augmenting the visual error feedback provided to subjects training in a point-to-point reaching task under visual distortion would improve the amount and speed of adaptation. Previous studies showing that human learning is error-driven and that visual error augmentation can improve the rate at which subjects decrease their trajectory error in such a task provided the motivation for our study. In a controlled experiment, subjects were required to perform point-to- point reaching movements in the presence of a rotational visual distortion. The amount and speed of their adaptation to this distortion were calculated based on two performance measures: trajectory error and hit time. We tested how three methods of error augmentation (error amplification, traditional error offsetting, and progressive error offsetting) affected the amount and speed of adaptation, and additionally propose definitions for {\textquotedblleft}amount{\textquotedblright} and {\textquotedblleft}speed{\textquotedblright} of adaptation in an absolute sense that are more practical than definitions used in previous studies. It is concluded that traditional error offsetting promotes the fastest learning, while error amplification promotes the most complete learning. Progressive error offsetting, a novel method, resulted in slower training than the control group, but we hypothesize that it could be improved with further tuning and indicate a need for further study of this method. These results have implications for improvement in motor skill learning across many fields, including rehabilitation after stroke, surgical training, and teleoperation.

When analyzed in the tangential speed domain, human movements exhibit a multi-peaked speed profile which is commonly interpreted as evidence for submovements. At slow speeds, the number of the peaks increases and the peaks also become more distinct, corresponding to non-smoothness or intermittency in the movement. In this study, we evaluate two potential sources proposed in the literature for the origins of movement intermittency and conclude that intermittency is more likely due to noise in the neuromuscular system as opposed to a central movement planner that generates intermittent plans. This conclusion is based on the assumption that the central planner would be expected to introduce similar levels of intermittency for different joints, while accumulating noise in the neuromuscular circuitry would be expected to exhibit itself as increase in noise in distal direction. We have used a 3D motion capture system to record trajectories of fingertip, wrist, elbow and shoulder as five participants completed a simple manual circular tracking task at various constant speed levels. Statistical analyses indicated that movement intermittency, quantified by a number of peaks metric, increased in distal direction, supporting the noise model for origins of intermittency. Movement speed was determined to have a significant effect on intermittency, while orientation of the task plane showed no significance.

In this paper, preliminary results in motor function improvement for four sub-acute stroke patients that underwent a hybrid robotic and traditional rehabilitation program are presented. The therapy program was scheduled for three days a week, four hours per day (approximately 60\% traditional constraint induced therapy activities and 40\% robotic therapy). A haptic joystick was used to implement four different operating modes for robotic therapy: unassisted (U), constrained (C), assisted (A), and resisted (R) modes. A target hitting task involving the positioning of a pointer on twelve targets was completed by the patients. Two different robotic measures were utilized to quantify the motor function improvement through the sessions: trajectory error (TE) and smoothness of movement (SM). Fugl-Meyer (FM) and motor activity log (MAL) scales were used as clinical measures. Analysis of results showed that the group demonstrates a significant motor function improvement with respect to both clinical and robotic measures. Regression analyses were carried out on corresponding clinical and robotic measure result pairs. A significant relation between FM scale and robotic measures was found for both of the analyzed modes. Regression of robotic measures on MAL scores resulted in no significance. A regression analysis that compared the two clinical measures revealed a very low agreement. Our findings suggest that it might be possible to obtain objective robotic measures that are significantly correlated to widely-used and reliable clinical measures in considerably different operating modes and control schemes.